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Record W2275329270

中国人群结直肠癌危险因素的 Meta 分析

2014· article· zh· W2275329270 on OpenAlexaboutno aff
邵红梅, 冯瑞, 朱红, 谢娟

Bibliographic record

Venue中国慢性病预防与控制 · 2014
Typearticle
Languagezh
FieldMedicine
TopicMedical Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness
DOInot available

Abstract

fetched live from OpenAlex

目的综合评价中国人群结直肠癌的危险因素,为结直肠癌的预防控制决策提供参考依据。方法计算机检索中国生物医学文献数据库、中国期刊全文数据库、维普数据库、万方数据库和PubMed,并辅以文献追溯方法,收集国内1985年1月至2012年11月公开发表的关于中国人群结直肠癌危险因素的研究文献,经Newcastle—Ottawa Seale(NOS)标准质量评价后采用RevMan5.1分析软件对入选的文献进行异质性检验,经Meta分析计算合并OR值及其95%CI。结果纳人合格研究文献25篇,累计病例6646例,对照9957例。Meta分析显示有统计学意义的结果:轻体力活动、饮茶、奶及其制品、葱蒜类食物、粗粮、蔬菜、水果等因素的合并OR值在0~1之间;吸烟、被动吸烟、情绪自我调节能力差、油炸或烟熏以及腌制食品、红肉、动物油、肥肉、饮食偏咸、痔疮史、胆囊疾病史、肿瘤家族史等因素的合并OR值在1-2之间;精神创伤史、烧烤食品、饮食油腻、阑尾炎史、胃及十二指肠溃疡、慢性结直肠炎、直系亲属肿瘤史、旁系亲属肿瘤史等因素的合并OR值在2-5之间;肠息肉、黏液血便、慢性便秘或腹泻等因素的合并OR值大于5。结论生活方式、精神刺激、烹调方式不当、饮食油腻等、肠道相关症状或疾病、家族肿瘤史与结直肠癌的发生有关;而轻体力活动、饮茶、奶制品、膳食纤维为保护性因素,为结直肠癌的早期预防提供了科学依据。

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.123
metaresearch head score (Gemma)0.352
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.123
Threshold uncertainty score0.649

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1230.352
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0190.015
Science and technology studies0.0030.008
Scholarly communication0.0150.017
Open science0.0040.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0210.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.102
GPT teacher head0.404
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2014
Admission routes1
Has abstractyes

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